Tag: Reasoning

All the articles with the tag "Reasoning".

• Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL

  Published: 20 May, 2025 |  at  11:10 AM

  

  92.09 🤔

  本文提出 *AutoThink*，通过省略号提示和多阶段强化学习框架，使 R1 风格大型推理模型根据问题复杂性自适应地决定是否进行显式推理，在五个数学基准上实现了准确性和效率的优越权衡。

• Temporal Sampling for Forgotten Reasoning in LLMs

  Published: 28 May, 2025 |  at  11:20 AM

  

  92.01 🤔

  本文揭示了大型语言模型微调中的'Temporal Forgetting'现象，并提出'Temporal Sampling'方法，通过从多个训练检查点采样答案显著提升推理性能（Pass@k提升4-19个百分点），并通过LoRA适配降低存储成本。

• From Words to Worlds: Compositionality for Cognitive Architectures

  Published: 25 May, 2025 |  at  11:24 AM

  

  91.89 🤔

  本文通过设计三种任务评估大型语言模型（LLMs）的组合性能力，发现模型规模扩大通常提升组合性表现，而指令微调效果不一致，提示组合性对性能提升的解释力有限。

• Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs

  Published: 17 May, 2025 |  at  11:02 AM

  

  91.74 🤔

  This paper introduces Learning to Think (L2T), an information-theoretic reinforcement fine-tuning framework for LLMs that uses a universal dense process reward to optimize reasoning effectiveness and efficiency, achieving significant accuracy and token efficiency gains on math reasoning benchmarks.

• Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models

  Published: 8 May, 2025 |  at  06:12 PM

  

  91.54 🤔

  This paper introduces Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning (LS-Mixture SFT), which combines long and short CoT datasets to fine-tune non-reasoning LLMs, achieving a 2.3% average accuracy improvement and 47.61% response length reduction on reasoning benchmarks.